Maximum-Entropy-Rate Selection of Features for Classifying Changes in Knee and Ankle Dynamics During Running
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Date
2017-06-02Author
Einicke, Garry A.
Sabti, Haider A.
Thiel, David V.
Fernández Andrés, Marta
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IEEE Journal of Biomedical and Health Informatics 22(4) : 1097-1103 (2018)
Abstract
This paper investigates deteriorations in knee and ankle dynamics during running. Changes in lower limb
accelerations are analyzed by a wearable musculo-skeletal monitoring system. The system employs a machine learning technique to classify joint stiffness. A maximum-entropyrate method is developed to select the most relevant features. Experimental results demonstrate that distance travelled and energy expended can be estimated from observed changes in knee and ankle motions during 5 km runs.